Estimation of subjective quality of life in schizophrenic patients using speech features
IntroductionPatients with schizophrenia experience the most prolonged hospital stay in Japan. Also, the high re-hospitalization rate affects their quality of life (QoL). Despite being an effective predictor of treatment, QoL has not been widely utilized due to time constraints and lack of interest....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Rehabilitation Sciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fresc.2023.1121034/full |
| _version_ | 1827919654084935680 |
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| author | Yuko Shibata John Noel Victorino Tomoya Natsuyama Naomichi Okamoto Reiji Yoshimura Tomohiro Shibata |
| author_facet | Yuko Shibata John Noel Victorino Tomoya Natsuyama Naomichi Okamoto Reiji Yoshimura Tomohiro Shibata |
| author_sort | Yuko Shibata |
| collection | DOAJ |
| description | IntroductionPatients with schizophrenia experience the most prolonged hospital stay in Japan. Also, the high re-hospitalization rate affects their quality of life (QoL). Despite being an effective predictor of treatment, QoL has not been widely utilized due to time constraints and lack of interest. As such, this study aimed to estimate the schizophrenic patients' subjective quality of life using speech features. Specifically, this study uses speech from patients with schizophrenia to estimate the subscale scores, which measure the subjective QoL of the patients. The objectives were to (1) estimate the subscale scores from different patients or cross-sectional measurements, and 2) estimate the subscale scores from the same patient in different periods or longitudinal measurements.MethodsA conversational agent was built to record the responses of 18 schizophrenic patients on the Japanese Schizophrenia Quality of Life Scale (JSQLS) with three subscales: “Psychosocial,” “Motivation and Energy,” and “Symptoms and Side-effects.” These three subscales were used as objective variables. On the other hand, the speech features during measurement (Chromagram, Mel spectrogram, Mel-Frequency Cepstrum Coefficient) were used as explanatory variables. For the first objective, a trained model estimated the subscale scores for the 18 subjects using the Nested Cross-validation (CV) method. For the second objective, six of the 18 subjects were measured twice. Then, another trained model estimated the subscale scores for the second time using the 18 subjects' data as training data. Ten different machine learning algorithms were used in this study, and the errors of the learned models were compared.Results and DiscussionThe results showed that the mean RMSE of the cross-sectional measurement was 13.433, with k-Nearest Neighbors as the best model. Meanwhile, the mean RMSE of the longitudinal measurement was 13.301, using Random Forest as the best. RMSE of less than 10 suggests that the estimated subscale scores using speech features were close to the actual JSQLS subscale scores. Ten out of 18 subjects were estimated with an RMSE of less than 10 for cross-sectional measurement. Meanwhile, five out of six had the same observation for longitudinal measurement. Future studies using a larger number of subjects and the development of more personalized models based on longitudinal measurements are needed to apply the results to telemedicine for continuous monitoring of QoL. |
| first_indexed | 2024-03-13T04:01:13Z |
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| institution | Directory Open Access Journal |
| issn | 2673-6861 |
| language | English |
| last_indexed | 2024-03-13T04:01:13Z |
| publishDate | 2023-03-01 |
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| series | Frontiers in Rehabilitation Sciences |
| spelling | doaj.art-94f76b21ec644819ba789d756ef99c232023-06-21T13:42:23ZengFrontiers Media S.A.Frontiers in Rehabilitation Sciences2673-68612023-03-01410.3389/fresc.2023.11210341121034Estimation of subjective quality of life in schizophrenic patients using speech featuresYuko Shibata0John Noel Victorino1Tomoya Natsuyama2Naomichi Okamoto3Reiji Yoshimura4Tomohiro Shibata5Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, JapanDepartment of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, JapanDepartment of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, JapanDepartment of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, JapanDepartment of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, JapanDepartment of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, JapanIntroductionPatients with schizophrenia experience the most prolonged hospital stay in Japan. Also, the high re-hospitalization rate affects their quality of life (QoL). Despite being an effective predictor of treatment, QoL has not been widely utilized due to time constraints and lack of interest. As such, this study aimed to estimate the schizophrenic patients' subjective quality of life using speech features. Specifically, this study uses speech from patients with schizophrenia to estimate the subscale scores, which measure the subjective QoL of the patients. The objectives were to (1) estimate the subscale scores from different patients or cross-sectional measurements, and 2) estimate the subscale scores from the same patient in different periods or longitudinal measurements.MethodsA conversational agent was built to record the responses of 18 schizophrenic patients on the Japanese Schizophrenia Quality of Life Scale (JSQLS) with three subscales: “Psychosocial,” “Motivation and Energy,” and “Symptoms and Side-effects.” These three subscales were used as objective variables. On the other hand, the speech features during measurement (Chromagram, Mel spectrogram, Mel-Frequency Cepstrum Coefficient) were used as explanatory variables. For the first objective, a trained model estimated the subscale scores for the 18 subjects using the Nested Cross-validation (CV) method. For the second objective, six of the 18 subjects were measured twice. Then, another trained model estimated the subscale scores for the second time using the 18 subjects' data as training data. Ten different machine learning algorithms were used in this study, and the errors of the learned models were compared.Results and DiscussionThe results showed that the mean RMSE of the cross-sectional measurement was 13.433, with k-Nearest Neighbors as the best model. Meanwhile, the mean RMSE of the longitudinal measurement was 13.301, using Random Forest as the best. RMSE of less than 10 suggests that the estimated subscale scores using speech features were close to the actual JSQLS subscale scores. Ten out of 18 subjects were estimated with an RMSE of less than 10 for cross-sectional measurement. Meanwhile, five out of six had the same observation for longitudinal measurement. Future studies using a larger number of subjects and the development of more personalized models based on longitudinal measurements are needed to apply the results to telemedicine for continuous monitoring of QoL.https://www.frontiersin.org/articles/10.3389/fresc.2023.1121034/fullquality of lifeschizophreniaspeech analysismachine learningmodel development |
| spellingShingle | Yuko Shibata John Noel Victorino Tomoya Natsuyama Naomichi Okamoto Reiji Yoshimura Tomohiro Shibata Estimation of subjective quality of life in schizophrenic patients using speech features Frontiers in Rehabilitation Sciences quality of life schizophrenia speech analysis machine learning model development |
| title | Estimation of subjective quality of life in schizophrenic patients using speech features |
| title_full | Estimation of subjective quality of life in schizophrenic patients using speech features |
| title_fullStr | Estimation of subjective quality of life in schizophrenic patients using speech features |
| title_full_unstemmed | Estimation of subjective quality of life in schizophrenic patients using speech features |
| title_short | Estimation of subjective quality of life in schizophrenic patients using speech features |
| title_sort | estimation of subjective quality of life in schizophrenic patients using speech features |
| topic | quality of life schizophrenia speech analysis machine learning model development |
| url | https://www.frontiersin.org/articles/10.3389/fresc.2023.1121034/full |
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